The concept of using pictures to understand data has been around for centuries, from maps and graphs in the 17th century to the invention of the pie chart in the early 1800s. Data visualization refers to the representation of data or information in charts, graphs, maps or other visual formats. Visualization helps stakeholders to see trends, recognize relationships and uncover outliers in their data. 

As Big Data kicks into high gear, visualization is a key tool to make sense of the trillions of rows of data generated every day. A good visualization tells a story, removing the noise from data and highlighting the useful information. However, this is no easy task. Effective data visualization is a mix between form and function. The data and the visuals need to work together, and there’s an art to combining great analysis with great storytelling.

It’s hard to think of a professional industry that doesn’t benefit from making data more understandable. Data visualization should be a key focus within an organization’s business intelligence (BI) strategy. If you choose the right visualization to highlight important aspects of your data, you can communicate them more persuasively.

So, what are some benefits of data visualization?

• Quicker decision-making. You can understand the story your data tells at a quick glance when you manipulate large datasets in visual formats.
• More data exploration. Data visualization tools allow you to interact with data to discover patterns, see relationships, etc. The best part – this can be done without the help of your IT team!
• Track business initiatives. Quickly set up dashboards to track key business initiatives and KPIs.

Data visualization tools can help you make better decisions and track business performance, but there are some challenges you and an organization may need to overcome.

• Lack of understanding. If the data doesn’t tell the right story, users won’t get value from it. It is crucial to understand your data to avoid telling incomplete, misleading or inaccurate stories.
• Lack of data governance. Many people within an organization feel comfortable using spreadsheets and ungoverned analytics tools to create their own presentations. To avoid inaccurate data stories or incomplete analyses, implement proper data governance practices.
• Clutter. Cramming too much data into one visual. Try to limit the number of KPIs in your dashboard, use pie charts for small datasets, select colors carefully and keep it simple.
• Reliance on manual process. When users don’t fully understand how data visualization tools can help them, they tend to create visualizations manually. This can result in mathematical errors, wasted hours and incorrect information. Take some time to learn how AI and machine learning can help automate repetitive tasks.

Data visualization will most likely change the way analysts work with data. Analysts will be expected to respond to issues more rapidly and they’ll need to be able to dig for more insights. Data visualization will promote that creative data exploration.

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Sources:

https://www.tableau.com/learn/articles/data-visualization#:~:text=Data%20visualization%20is%20the%20graphical,outliers%2C%20and%20patterns%20in%20data.

https://www.qlik.com/us/data-visualization